US geospatial crop frequency data layers

Boryan, Claire G.; Yang, Zhengwei; Willis, Patrick

doi:10.1109/agro-geoinformatics.2014.6910657

Cited by 15 publications

(12 citation statements)

References 7 publications

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“…Corn is the primary crop grown in SD and is selected as the basis for the stratification design. The method to create and validate the crop frequency layers is described in Reference [1]. The method to create and validate the NASS cultivation layer is described in Reference [2].…”

Section: Methodsmentioning

confidence: 99%

“…The SD 2008 -2013 crop frequency layers were derived from 2008-2013 CDL data. They capture planting frequency at the pixel level over time [1]. When intersected at the PSU level, the crop frequency data provide unique information on planting probabilities.…”

Section: Datamentioning

confidence: 99%

“…However, percent cultivation information does not provide predictive crop planting information, which is critical for estimation of specific crops. This paper presents a novel land cover stratification using NASS geospatial crop planting frequency data layers [1], and the NASS cultivation layer [2] which are derived from historic Cropland Data Layers (CDLs) [3], to improve the accuracy, objectivity, efficiency of the NASS ASFs and ultimately crop survey estimates. The goal of this research is to determine 1) if the distribution of the primary sampling units (PSUs) in the new ASF design based on corn planting frequency and cultivation, is consistent with the PSU distribution in the current NASS ASF based on percent cultivation alone and 2) whether the corn frequency and cultivation stratification can predict patterns of future crop specific planting.…”

Section: Introductionmentioning

confidence: 99%

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A novel method for area frame stratification based on geospatial crop planting frequency data layers

Boryan

Yang

Willis

2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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This paper proposes a novel method for land cover area frame stratification based on corn planting frequency and percent cultivation. South Dakota U.S. geospatial crop frequency (2008)(2009)(2010)(2011)(2012)(2013) and cultivation (2013) data layers created from NASS Cropland Data Layers are utilized to develop a novel area sampling frame (ASF) stratification design. Eight corn planting frequency strata are derived using a k-means clustering method based on mean corn planting frequency calculated at the NASS ASF primary sampling unit level. The corn planting frequency strata are then sub stratified based on percent cultivation, which, together, provide more crop specific information than the current NASS ASF based on percent cultivation alone. Using 2014 Farm Service Agency Common Land Unit Data as in situ validation, it is found that this novel ASF design predicts crop specific planting patterns well. These results indicate that the new stratification method has potential to improve ASF accuracy, efficiency and crop estimates.

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Section: Methodsmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel method for area frame stratification based on geospatial crop planting frequency data layers

Boryan

Yang

Willis

2015

2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

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“…The CDL, which is a raster, geo-referenced, crop-specific land cover data layer with a ground resolution of 30 m, is widely accepted as a crop-type reference data layer in crop classification studies [32][33][34][35]. The CDL program was developed by the US Department of Agriculture (USDA), National Agricultural Statistics Service (NASS) [36]. We randomly selected training and validation data from the CDL.…”

Section: Datamentioning

confidence: 99%

Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

et al. 2020

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Accurate cropland classification is important for agricultural monitoring and related decision-making. The commonly used input spectral features for classification cannot be employed to effectively distinguish crops that have similar spectro-temporal features. This study attempted to improve the classification accuracy of crops using both the thermal feature, i.e., the land surface temperature (LST), and the spectral feature, i.e., the normalized difference vegetation index (NDVI), for classification. To amplify the temperature differences between the crops, a temperature index, namely, the modified land surface temperature index (mLSTI) was built using the LST. The mLSTI was calculated by subtracting the average LST of an image from the LST of each pixel. To study the adaptability of the proposed method to different areas, three study areas were selected. A comparison of the classification results obtained using the NDVI time series and NDVI + mLSTI time series showed that for long time series from June to November, the classification accuracy when using the mLSTI and NDVI time series was higher (85.6% for study area 1 in California, 96.3% for area 2 in Kansas, and 91.2% for area 3 in Texas) than that when using the NDVI time series alone (82.0% for area 1, 94.7% for area 2, and 90.9% for area 3); the same was true in most of the cases when using the shorter time series. With the addition of the mLSTI time series, the shorter time series achieved higher classification accuracy, which is beneficial for timely crop identification. The sorghum and soybean crops, which exhibit similar NDVI feature curves in this study, could be better distinguished by adding the mLSTI time series. The results demonstrated that the classification accuracy of crops can be improved by adding mLSTI long time series, particularly for distinguishing crops with similar NDVI characteristics in a given study area.

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“…Trap sites were spaced between 2.8 and 31.4 km with individual traps at each site placed approximately 150 to 1,000 m apart to ensure that they were operating independently. Corn and dry bean abundance within a 5 km and 50 km radius surrounding the trap sites was determined using Cropscape , Han et al 2012, Boryan et al 2011, Boryan et al 2014 ( Table 1 and Table 2). Individual trap sites were used for the 5 km radius determination.…”

Section: Sample Collectionmentioning

confidence: 99%

Contribution of larvae developing on corn and dry beans to the adult population of western bean cutworm in Michigan

Bunn

Oliveira

Springborn

et al. 2020

Preprint

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The western bean cutworm, Striacosta albicosta (Smith) (Lepidoptera: Noctuidae), is historically a pest of both corn (Zea maize L.) and dry beans in the western Great Plains. However, it has recently undergone an eastward range expansion establishing itself across the corn belt in twenty-five states and four Canadian provinces. To mitigate the effects of infestation in Michigan, foliar insecticides are used in beans whereas management of the pest in corn relies more heavily on the use of Bt-expressing hybrids. In this study stable carbon isotope analysis was used to determine what crop adult moths developed on as larvae with analysis showing that very few of the adult moths developed on beans in their larval state. These results suggest that beans and corn are not suitable as co-refuges and that mainly adults which developed on corn are contributing to the next generation of western bean cutworm in Michigan.

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US geospatial crop frequency data layers

Cited by 15 publications

References 7 publications

A novel method for area frame stratification based on geospatial crop planting frequency data layers

A novel method for area frame stratification based on geospatial crop planting frequency data layers

Improving the Classification Accuracy of Annual Crops Using Time Series of Temperature and Vegetation Indices

Contribution of larvae developing on corn and dry beans to the adult population of western bean cutworm in Michigan

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